ANALISIS SENTIMEN HATESPEECH PADA TWITTER DENGAN METODE NAÃVE BAYES CLASSIFIER DAN SUPPORT VECTOR MACHINE - Buntoro, Ghulam Asrofi (Dinamika Informatika, 2016)
Today, social media, especially Twitter have enormous influence to the success or ruin the image of a person. Many movements are carried out in social media, especially Twitter, all of which can influence its success. There is a movement that aims good there is also a movement with malicious purposes, namely hatred to others. Usually the movement on Twitter was done using the hashtag (#), the latest movement there tagar Hatespeech (#HateSpeech), viewed from the name is already clear that hate speech. This study analyzes the hashtag proficiency level, all justified by the hashtag was the sentiment of hate. The classification process in this study using the method of classification Naive Bayes classifier (NBC) and Support Vector Machine (SVM) with the data preprocessing using tokenisasi, cleansing and filtering. The data used are in Indonesian tweet with the hashtag HateSpeech (#HateSpeech), with the number of datasets as much as 522 tweets were distributed evenly into two sentiments HateSpeech and GoodSpeech. The highest accuracy of results obtained when using the method of classification Support Vector Machine (SVM) with tokenisasi unigram, stopword list Indonesian and emoticons, with the average value reached 66.6% accuracy, precision value of 67.1%, 66.7% recall value TP value rate of 66.7% and 75.8% rate the value TN.